# classvision_onnx

**Repository Path**: li_hua_qiang/classvision_onnx

## Basic Information

- **Project Name**: classvision_onnx
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-06
- **Last Updated**: 2021-07-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# grids_detection

detecting the grids on the shelf

# framework

yolo_v5 pytorch

# details

model: yolov5s

size: 29Mb

input size : 512,512,3

resize method: padding with color (0,0,0)

# model
copy model file from [NAS]:/research/algorithm/tobacco/grids_detection/gridDet_0.2.0.onnx to data directory under this project.

# result check
python -m examples.demo

[ [1.81900000e+03 6.36000000e+02 3.06600000e+03 1.12700000e+03  9.54117894e-01 0.00000000e+00]  
 [1.82500000e+03 1.13600000e+03 3.06600000e+03 1.57900000e+03
 9.51087475e-01 0.00000000e+00]  
 [3.18500000e+03 1.57700000e+03 4.42600000e+03 2.02200000e+03
 9.50353622e-01 0.00000000e+00]  
 [4.35000000e+02 1.13700000e+03 1.67700000e+03 1.58900000e+03
 9.48073506e-01 0.00000000e+00]  
 [3.19300000e+03 6.43000000e+02 4.42800000e+03 1.14200000e+03
 9.47899163e-01 0.00000000e+00]  
 [3.19900000e+03 1.13300000e+03 4.43100000e+03 1.59200000e+03
 9.47785616e-01 0.00000000e+00]  
 [4.12000000e+02 6.19000000e+02 1.68800000e+03 1.13900000e+03
 9.47220623e-01 0.00000000e+00]  
 [4.37000000e+02 1.58100000e+03 1.68100000e+03 2.03400000e+03
 9.46251392e-01 0.00000000e+00]  
 [1.82700000e+03 1.58200000e+03 3.06200000e+03 2.02700000e+03
 9.46055532e-01 0.00000000e+00]  
 [0.00000000e+00 6.22000000e+02 2.73000000e+02 1.13000000e+03
 9.37542200e-01 0.00000000e+00]  
 [3.00000000e+00 1.12500000e+03 2.74000000e+02 1.58200000e+03
 9.36134756e-01 0.00000000e+00]  
 [2.00000000e+00 1.58200000e+03 2.84000000e+02 2.01400000e+03
 9.28792477e-01 0.00000000e+00]  
 [8.14000000e+02 2.58200000e+03 3.14200000e+03 3.37700000e+03
 9.18584943e-01 0.00000000e+00]  
 [1.83000000e+03 2.02700000e+03 3.04500000e+03 2.45400000e+03
 9.12666261e-01 0.00000000e+00]  
 [3.18600000e+03 2.01500000e+03 4.41500000e+03 2.46800000e+03
 9.09129322e-01 0.00000000e+00]]